Conventional filtering techniques or related techniques that could be used for filtering of M2M data can be assigned to one of the following three categories: The first category is related to general-purpose filtering and aggregation methods which perform removal or aggregation of duplicate data, erroneous data, outliers, etc. as it is shown for example in the non-patent literature of O. Mylyy, “RFID Data Management, Aggregation and Filtering,” 2008, Hasso Plattner Institute Publications, Seminar on RFID Technology or of O. Wedin, J. Bogren, and I. Grabec, “Data Filtering Methods,” 2008, EU Project Deliverable, Roadidea 215455.
In the second category quality of information QoI assessment methods are subsumed which may be domain-specific, score sheet-based, etc. for evaluating how much information is contained in a data set or how important this information is. In the non-patent literature of M. A. Hossain, P. K. Atrey, and A. El-Saddik, “Modeling and Assessing Quality of Information in Multisensor Multimedia Monitoring Systems,” ACM Transactions on Multimedia Computing, Communications and Applications (TOMCCAP), vol. 7, no. 1, pp. 3:1-3:30, 2011 algorithms are disclosed for evaluating the importance of captured multimedia content. In the further non-patent literature of S. Zöller, A. Reinhardt, S. Schulte, and R. Steinmetz, “Scoresheet-based Event Relevance Determination for Energy Efficiency in Wireless Sensor Networks,” in IEEE Conference on Local Computer Networks (LCN). EDAS Conference Services, 2011, pp. 207-210 an assessment and filtering of sensor readings is disclosed based on score-sheets of a provider while in the non-patent literature of B. Stvilia, L. Gasser, M. B. Twidale, and L. C. Smith, “A Framework for Information Quality Assessment,” Journal of the American Society for Information Science and Technology, vol. 58, no. 12, pp. 1720-1733, 2007 a general framework that can be customized for assessing quality of information in different use cases is shown.
In the third category filtering is conventionally performed based on a data classification. For example in the non-patent literature of D. Chu, N. D. Lane, T. T.-T. Lai, C. Pang, X. Meng, Q. Guo, F. Li, and F. Zhao, “Balancing Energy, Latency and Accuracy for Mobile Sensor Data Classification,” in ACM Conference on Embedded Networked Sensor Systems (SenSys '11). ACM, 2011, pp. 54-67 and in the non-patent literature of M. Rahman, Y. Lazim, F. Mohamed, “Applying Rough Set Theory in Multimedia Data Classification”, International Journal on New Computer Architectures and their Applications (IJNCAA), Vol. 1, No. 3, pages 683-693, The Society of Digital Information and Wireless Communications, 2011 standard machine learning techniques are disclosed in order to classify readings of different sensors like sound recordings, images or GPS series, into various different data categories. Certain data categories can then be forwarded while others are filtered.